Bragg grating fabrication in microfiber by femtosecond pulse filamentation induced periodic refractive index modification
Bibliographic record
Abstract
Fabrication of fiber Bragg grating (FBG) in single mode microfiber (core diameter: 3.75 μm, cladding diameter: 40 μm) by femtosecond laser pulse radiation is presented. Femtosecond pulse filamentation technique is employed in the pointby-point writing method to inscribe single shot periodic index modification in the core of the microfiber. Prior to writing gratings, a short length (~1.5 mm) of microfiber is fusion spliced between two standard single mode fibers (SMF) in order to improve handling and ease grating fabrication. The kilohertz femtosecond laser pulses operating at center wavelength of 800 nm were tightly focused with an objective lens (40X/ NA=0.75) to confine the pulses into a very tiny focal volume and spatially control index modification. The focused femtosecond pulses create filamentary voids at focal point. For the scanning speed of 534 nm/ Sec, the partial overlapping of void structures produces a periodic index modification in the core with a period of 534 nm and constructs the Bragg reflection spectrum centered at 1550.216 nm. Fabrication of a 1 mm long FBG takes less than 2 seconds for the scanning speed of 0.534 mm/sec. The spectral position of Bragg reflection spectrum can easily be tailored simply by changing pulse scanning speed. The performance analysis of the FBG is examined for temperature and axial strain sensitivity. The grating sensor exhibits the temperature and strain sensitivity of 10 pm/ °C and ~1 pm/ micro-strain, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".